Analysis#

class osekit.public_api.analysis.Analysis(analysis_type: AnalysisType, begin: Timestamp | None = None, end: Timestamp | None = None, data_duration: Timedelta | None = None, mode: Literal['files', 'timedelta_total', 'timedelta_file'] = 'timedelta_total', sample_rate: float | None = None, name: str | None = None, subtype: str | None = None, fft: ShortTimeFFT | None = None, v_lim: tuple[float, float] | None = None, colormap: str | None = None, scale: Scale | None = None, nb_ltas_time_bins: int | None = None)#

Class that contains all parameter of an analysis.

Analysis instances are passed to the public API dataset, which runs the analysis. The Analysis object contains all info on the analysis to be done: the type(s) of core_api dataset(s) that will be created and added to the Dataset.datasets property and which output files will be written to disk (reshaped audio files, npz spectra matrices, png spectrograms…) depend on the analysis_type parameter. The Analysis instance also contains the technical parameters of the analyses (begin/end times, sft, sample rate…).

Initialize an Analysis object.

Parameters#

analysis_type: AnalysisType: The type of analysis to run. See AnalysisType docstring for more info.
begin: Timestamp | None: The begin of the analysis dataset. Defaulted to the begin of the original dataset.
end: Timestamp | None: The end of the analysis dataset. Defaulted to the end of the original dataset.
data_duration: Timedelta | None: Duration of the data within the analysis dataset. If provided, audio data will be evenly distributed between begin and end. Else, one data object will cover the whole time period.
mode: Literal[“files”, “timedelta_total”, “timedelta_file”]: Mode of creation of the dataset data from the original files. “files”: one data will be created for each file. “timedelta_total”: data objects of duration equal to data_duration will be created from the begin timestamp to the end timestamp. “timedelta_file”: data objects of duration equal to data_duration will be created from the beginning of the first file that the begin timestamp is into, until it would resume in a data beginning between two files. Then, the next data object will be created from the beginning of the next original file and so on.
sample_rate: float | None: Sample rate of the new analysis data. Audio data will be resampled if provided, else the sample rate will be set to the one of the original dataset.
name: str | None: Name of the analysis dataset. Defaulted as the begin timestamp of the analysis dataset. If both audio and spectro analyses are selected, the audio analysis dataset name will be suffixed with “_audio”.
subtype: str | None: Subtype of the written audio files as provided by the soundfile module. Defaulted as the default 16-bit PCM for WAV audio files. This parameter has no effect if Analysis.AUDIO is not in analysis.
fft: ShortTimeFFT | None: FFT to use for computing the spectra. This parameter is mandatory if either Analysis.MATRIX or Analysis.SPECTROGRAM is in analysis. This parameter has no effect if neither Analysis.MATRIX nor Analysis.SPECTROGRAM is in the analysis.
v_lim: tuple[float, float] | None: Limits (in dB) of the colormap used for plotting the spectrogram. Has no effect if Analysis.SPECTROGRAM is not in analysis.
colormap: str | None: Colormap to use for plotting the spectrogram. Has no effect if Analysis.SPECTROGRAM is not in analysis.
scale: osekit.core_api.frequecy_scale.Scale: Custom frequency scale to use for plotting the spectrogram. Has no effect if Analysis.SPECTROGRAM is not in analysis.
nb_ltas_time_bins: int | None: If None, the spectrogram will be computed regularly. If specified, the spectrogram will be computed as LTAS, with the value representing the maximum number of averaged time bins.

property is_spectro: bool#: Return True if the analysis contains spectral computations, False otherwise.

class osekit.public_api.analysis.AnalysisType(*values)#

Enum of flags that should be use to specify the type of analysis to run.

AUDIO:: Will add an AudioDataset to the datasets and write the reshaped audio files to disk. The new AudioDataset will be linked to the reshaped audio files rather than to the original files.
MATRIX:: Will write the npz SpectroFiles to disk and link the SpectroDataset to these files.
SPECTROGRAM:: Will export the spectrogram png images.
WELCH:: Will write the npz welches to disk.

Multiple flags can be enabled thanks to the logical or | operator: AnalysisType.AUDIO | AnalysisType.SPECTROGRAM will export both audio files and spectrogram images.

>>> # Exporting both the reshaped audio and the spectrograms
>>> # (without the npz matrices):
>>> export = AnalysisType.AUDIO | AnalysisType.SPECTROGRAM
>>> AnalysisType.AUDIO in export
True
>>> AnalysisType.SPECTROGRAM in export
True
>>> AnalysisType.MATRIX in export
False

Analysis

Contents

Analysis#

Parameters#